Regulation of detonation ability of explosive materials used in spacecraft separation systems

Kotomin, A. A.; Kozlov, A. S.; Gorovtsov, V. V.; Efanov, V. V.; Trapeznikov, M. A.; Dushenok, S. A.; Breshev, E. N.

doi:10.1134/s0038094612070155

Cited by 8 publications

(6 citation statements)

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“…. , ), then compute the membership that the sample belongs to the th class according to (9) and then confirm the class of the sample according to (10). Consider…”

Section: Multiclass Incremental Learning Algorithmmentioning

confidence: 99%

“…If there are no less than two hyperellipsoidals containing the sample point, compute the membership that the sample belongs to the th class according to (11) for ( , ) ≤ firstly and then confirm the class of the sample according to (10). One has = 1 − ( , ) .…”

Section: Multiclass Incremental Learning Algorithmmentioning

confidence: 99%

“…When using SVMs to deal with multiclassification problems, the most popular four approaches are one-againstone [6], one-against-rest [7], DAGSVM [8], and binary tree SVM [9][10][11]. Some incremental learning algorithms have been proposed, such as Batch SVM [12,13], online recursive algorithm [14], divisional training SVMs algorithm [15], and fast incremental learning algorithm [16].…”

Section: Introductionmentioning

confidence: 99%

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A New SVM Multiclass Incremental Learning Algorithm

Qin

Zhang

2015

Mathematical Problems in Engineering

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A new support vector machine (SVM) multiclass incremental learning algorithm is proposed. To each class training sample, the hyperellipsoidal classifier that includes as many samples as possible and pushes the outlier samples away is trained in the feature space. When the new samples are added to the classification system, the algorithm reuses the old classifiers that have nothing to do with the new sample classes. To be classified sample, the Mahalanobis distances are used to decide the class of classified sample. If the sample point is not surrounded by any hyperellipsoidal or is surrounded by more than one hyperellipsoidal, the membership is used to confirm its class. The experimental results show that the algorithm has higher performance in classification precision and classification speed.

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“…. , ), then compute the membership that the sample belongs to the th class according to (9) and then confirm the class of the sample according to (10). Consider…”

Section: Multiclass Incremental Learning Algorithmmentioning

confidence: 99%

Section: Multiclass Incremental Learning Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A New SVM Multiclass Incremental Learning Algorithm

Qin

Zhang

2015

Mathematical Problems in Engineering

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“…3, 4-dinitrofurazanofuroxan (DNTF) is a high explosive, whose crystal density is 1.937 g cm -3 and detonation velocity is more than 9000 ms À1 , have attracted many scientists and been used in propellants and PBX explosives [14]. Moreover, DNTF was reported to exhibit a smaller critical size of detonation than CL-20 [15]. These all make it a promising replacement of CL-20 used in all-liquid explosive ink.…”

Section: Introductionmentioning

confidence: 99%

Direct Ink Writing of DNTF Based Composite with High Performance

et al. 2018

Propellants Explo Pyrotec

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Direct ink writing (DIW) of energetic materials has been an area of interest for micro size charge. In this work, 3, 4‐dinitrofurazanofuroxan (DNTF) based composite was prepared with nitrocotton (NC) and Viton as binders by DIW. Scanning Electro Microscope (SEM) and X‐ray diffraction were employed to characterize the composite samples. The impact sensitivity and thermal decomposition of the composites were also tested and analyzed. In addition, the critical size of detonation and detonation velocity were measured. The results show that DNTF based composite has a high density with whose value is 1.785 g cm−3, reaching 93.16 % of theoretical maximum density (TMD). The particles in composites are spheroidal with size ranging from 1 to 2 μm. Compared with raw DNTF, the obtained composite has a lower impact sensitivity and higher thermal stability. Moreover, the composites exhibit excellent detonation properties, whose critical size of detonation is around 0.01 mm and the mean detonation velocity is 8580 m s−1 at the charging width of 1 mm. Furthermore, part performances of this composite are contrasted with previous reported CL‐20 based composite and show better characters.

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“…Compared with CL-20, PETN does not possess any advantages in crystal density and energy. PETN is a promising replacement in DIW because of its critical detonation size, which is reportedly smaller than that of CL-20, and this property contribute to the detonation transition in the micro size groove [13]. Moreover, crystal morphology of PETN is more steady than that of CL-20 during manufacture.…”

Section: Introductionmentioning

confidence: 99%

Preparation and Performance of Pentaerythrite Tetranitrate‐Based Composites by Direct Ink Writing

et al. 2018

Propellants Explo Pyrotec

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Direct ink writing (DIW), a promising technology for manufacturing energetic materials, has been a hot topic in the micro-scale charge of explosives. Herein, three kinds of pentaerythrite tetranitrate (PETN)-based all-liquid explosive inks were engineered and patterned using DIW. Scanning electron microscopy, energy-dispersive x-ray spectroscopy, X-ray diffraction, differential scanning calorimetry, and nanoindentation were used to characterize the printed samples. The density, thickness of single layer, impact sensi-tivity and critical size of detonation were measured and analyzed. Results show that PETN/EC/Viton exhibits excellent properties, specifically greater activation energy (148.09 KJ mol À1 ), elastic modulus (2.808 GPa), and characteristic height (49.9 cm, 2.5 kg), over the two other samples. Moreover, a directly deposited sample in small grooves can provide steady detonation above the size of 1 3 0.101 mm. These features all contribute to the application of microsize detonation to a certain degree.

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Regulation of detonation ability of explosive materials used in spacecraft separation systems

Cited by 8 publications

References 0 publications

A New SVM Multiclass Incremental Learning Algorithm

A New SVM Multiclass Incremental Learning Algorithm

Direct Ink Writing of DNTF Based Composite with High Performance

Preparation and Performance of Pentaerythrite Tetranitrate‐Based Composites by Direct Ink Writing

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